Market Segmentation and the Myth of Demographics

Market SegmentationDo you really believe that all 24-36 year old men buy your product for the same reason? If you do, I have a slightly used bridge at a very attractive price for you.

Demographic segmentation strategy is based on the assumption that a specific group – based on age, gender, etc. – is the primary consumer of your product or service. Regardless of the validity of this assumption, it does not often provide insight on why this demographic segment selects the product in question or how they use it. For that reason segmenting a market by demographics has very limited utility. It has become so popular only because no better intelligence about customers was available at the time it was introduced.

Today, a much better approach to market segmentation is available. Grouping potential customers according to the expectations they would have from a proposed product is much more useful. This outside-in approach is similar to the “persona” concept often used by product managers, but uses actual market intelligence instead of imaginary characters.

The first step is identification of “the job-to-be-done” by the proposed product to be “hired” by the customers.

The second step is identification of the products/services the customers use today to do that “job”. This list will likely include products/services that you would not normally consider your competition, but customers may.

The third step is aggregation and analysis of a statistically representative set of “stories” describing the experience of customers who have used currently marketed products/services to do that job. I use the word “stories” deliberately to describe unsolicited and unstructured descriptions of the experiences in the customer’s own words. Any use of survey or focus group methods will “color” the output with a 3rd party bias. The best content is customer biased only.

The result will expose the attributes of the experience that are most important from the customers’ perspective. It will also provide an assessment of how well each attribute met the expectations of these customers. Focus on the attributes with low scores may provide important insight for designing a product that is very likely to take this market segment by storm.

Click here to request a copy of the Mining Social Media to Boost Segmentation paper published by QUIRK’S Marketing Research Review.

If demographic data is pertinent for your product, you can compile it’s distribution by an attribute to improve your chances for success even further. As the GPS
technology taught us: The multiplicity of signal sources results in better decision quality.

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8 Responses to Market Segmentation and the Myth of Demographics

  1. Eleftherios Mantelas says:

    This is an interesting post Gregory and I believe as well that there are better options than segmenting purely on socio-demographics. At the same time, I would just not play down the importance of socio-demographics – not that much anyway. Without being in my intentions to provide a complete analysis, I would like to mention a couple of points. Obviously, not all 24-36 year old men behave the same but there is rather solid evidence that they behave in average much different than 18-24 or 37-58 year old men. What is more, while even this break down might not be really helpful, adding other dimensions of information such as education, occupation, income, marital status and location would compose an entirely different picture. If such demographic data were usually available and of good quality, then I would have another argument to bring up, but unfortunately they simply aren’t. No, how can I get more information about that bridge?

  2. Mark A. Biernbaum, PhD says:

    The methods proposed very briefly in the article seem to simply be saying that qualitative data, in the form of interviews or stories, can help market researchers find meaningful groupings when in the midst of new product development. In Education and other fields, survey research, that includes pertinent demographics, is often “backed up” by qualitative analysis of interview or focus group data. It’s referred to as the Mixed Method approach. The qualitative data is there to provide a richer subtext than is possible to achieve using a survey. And there are systematic methods of analysis for qualitative data, including things like Grounded Theory code development and Abstracting. Using the stories of consumers alone does carry some risk, because the data from individual consumers is anecdotal and cannot be generalized beyond the demographic characteristics of the people whose stories are sampled. A combination of the two approaches is ideal.

  3. Gregory says:

    Eleftherios, the post is exploring relative importance of socio-demographics for product marketing. From this perspective, the behavior patterns of specific groups are less important than the reason of why the members of such groups would want to use a proposed product and to a lesser degree, how they would use it. It is a different focus on the inquiry in my opinion.

    The availability of good socio-demographic information can only bring more value and help to produce better decision.

  4. Gregory says:

    Mark, I propose that a high volume of qualitative data can be transformed into quantitative data by opinion mining methods and/or technology. That approach provides algorithmic grouping which is customer biased, as oppose to a survey that is inevitably contains a bias of a question creator. The findings can be backed/validated by traditional quant methods, if desired.

    “the data from individual consumers is anecdotal” – usually people use this argument when the data is not statistically representative. Availability of millions consumer stories, describing their factual experiences, can hardly be deemed deficient in representation.

  5. Mark A. Biernbaum, PhD says:

    Just because you have stories from millions of consumers does not, in itself, guarantee that your subjects are “representative.” First, you do not say what you believe they are representative of, and of course, that information is vitally important.

    The size if your group does not mean that they are representative of anything but consumers who have made a certain purchase. Also, there is no doubt at all that some consumers were not interested in sharing their story, so regardless of group size, your sample is not representative of people who made purchases but declined the opportunity to tell their stories, and you have no idea how large that group is and why they are different.

    Turning qualitative data into quantitative data has been done for decades- mostly through Grounded Theory branched coding analyses where you can establish inter-rater reliability.

  6. Gregory says:

    Mark, in the context of the product marketing research, the size of a group relates to a number of products sold, and therefore may or may not be representative. If common groupings are emerging, that is representative that the product met od did not meet the customer’s expectations. The sentiment analysis of these groupings (attributes of CX) shows the answer to that question. There are also well observed/measured correlations between a total number of people who purchased a product and a number (subset) of customers who decided to share their experiences.

    The point I was trying to make is that type of analysis can flash out unmet latent customer needs, while analysis of demographics, as it is practiced in product management, cannot.

    “Ounce of insight worth tons of research”

  7. Demographics is where the readily available data is, so your right that traditional segmentation trends to revolve around it. Psychographic segmentation is usually the way to go, although the two aren’t mutually exclusive. You can segment along psychographic lines, correlate psychographics to demographics, and size using available the demographic data.

    “Jobs to be done” do provide important context, but I segment by the relevant set of problems that segments of the market face.

  8. Gregory says:

    Roger, there are no single tool/method for every task. What we really discuss here is – what is the “first” step on the journey of defining the product/service. That is far from trivial choice and has major implications on probability of success, i.e. a scale of adoption of the product/service by its market.

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